INTRODUCTION TO NONLINEAR OPTIMIZATION Theory, Algorithms, and Applications with MATLAB. 讲解非线性优化方法的书籍。
2021-07-20 11:42:37
58.79MB
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线性优化
还记得你的线性优化课吗？
“一家企业生产 2 种产品。每种产品需要不同数量的 3 种原料，但供应有限。两种产品都会产生特定的利润，而且工厂产能存在整体限制。理想的产品组合是什么？”
这大致就是我们必须在课堂上解决的练习，就是这样。 我们使用的单纯形算法太复杂，无法真正教授，所以我们只需要记住步骤并应用它。
哦，我们短暂地听说过，显然人们正在使用它。 但是我们没有深入研究这些，无论如何，纸笔单纯形法对此没有用处。
这当然是不能接受的，所以我做了linear-optimization.com
使用 [Microsoft Solver Foundation] ( )（已弃用，因此对于下一个项目，我可能会使用 ) 我为各种问题类型和复杂性/难度级别随机创建了有趣的线性优化问题。 用户可能会尝试解决并输入他们的答案并获得一些反馈。
笔记：
关注可访问性。 早期的问题可以通过尝
2021-07-06 21:03:15
19.26MB
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非线性优化经典著作——《methods for nonlinear least square problems》
2021-05-23 11:02:14
1.8MB
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by Johannes Jahn (Author)
This book serves as an introductory text to optimization theory in normed spaces. The topics of this book are existence results, various differentiability notions together with optimality conditions, the contingent cone, a generalization of the Lagrange multiplier rule, duality theory, extended semidefinite optimization, and the investigation of linear quadratic and time minimal control problems. This textbook presents fundamentals with particular emphasis on the application to problems in the calculus of variations, approximation and optimal control theory. The reader is expected to have a basic knowledge of linear functional analysis.
2021-05-19 23:10:05
8.78MB
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BF共轭梯度法优化无约束非线性问题，求函数极小值，理论和例子参考《最优化方法》（北京理工大学出版社）（程序为自编！！！请不要下架！！！）。压缩包内含matlab程序文件（直接运行BFCG.m），Word文档算例说明.docx。结构化程序易于扩展。
2021-05-12 16:39:35
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一本介绍非线性优化理论的英文原版书，施普林格出版社出版的，值得阅读。
2021-04-05 21:28:02
8.61MB
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线性规划讲课大纲
LINEAR PROGRAMMING
Outline
• Course objective
• Pre-requisites
• Course content
• Grading and exams • Classroom rules
• Textbook and references
LECTURE 1: INTRODUCTION
Outline
• What is Linear Programming?
• Why to study Linear programming?
• How to study Linear Programming?
• History of Linear Programming
• How to solve an LP problem?
• Where to go?
LECTURE 2: PRELIMINARIES
1. Standard form LP
2. Embedded assumptions
3. Converting to standard form
LECTURE 3: GEOMETRY OF LP
1. Terminologies
2. Background knowledge
3. Graphic method
4. Fundamental theorem of LP
LECTURE 4: SIMPLEX METHOD
1. Simplex method
2. Phase one method
3. Big M method
LECTURE 5: DUALITY AND SENSITIVITY ANALYSIS
1. Dual linear program
2. Duality theory
3. Sensitivity analysis
4. Dual simplex method
LECTURE 6: INTERIOR POINT METHOD
1. Motivation
2. Basic concepts
3. Primal affine scaling algorithm
4. Dual affine scaling algorithm
LECTURE 7: ROBUST LINEAR OPTIMIZATION
1. Motivation
2. Robust model
3. Solution methods
2021-03-29 15:53:12
44.43MB
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